Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
Few-shot Learning for Intelligent Multimedia Systems

Few-shot Learning for Intelligent Multimedia Systems

Journal
Impact Score 1.81

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 15-11-2021
Journal Impact Score: 1.81
Journal Name: Multimedia Systems
Publisher: Multimedia Systems
Journal & Submission Website: https://www.springer.com/journal/530

Special Issue Call for Papers

Guest Editors

Jiachen Yang, Tianjin University, China ([email protected])

Houbing Song, Embry-Riddle Aeronautical University, USA ([email protected])

Qinggang Meng, Loughborough University, UK ([email protected])

Aims and scope

Multimedia data is all around us, e.g. videos, images, texts, etc. In recent years, we have achieved many excellent performances on the processing and analysis of multimedia data through deep learning. However, the current deep learning method relies on quite large-scale datasets, which is time-consuming to annotate and seems still far away from our desired intelligence. Considering the way humans how to deal with multimedia data, such as classification of images or videos, we can easily complete the recognition task from only a handful of data rather than millions of data. As for the intelligent multimedia systems, the knowledge-driven is clearly more appropriate than data-driven. So, it is still challenging for the existed techniques to complete intelligent pattern recognition from only a few labelled multimedia data, called few-shot learning, which aims to develop a model with good generalization based on a few samples. But the few-shot learning for intelligent multimedia systems should be a fundamental step towards the desired artificial intelligence, as it pursues the combination of natural intelligence with algorithm flexibility and extensibility.

The goal of this special issue is to assemble recent advances in the few-shot learning based multimedia analysis and processing. The multimedia data of interest covers a wide spectrum, ranging from images, texts, audios, to kinds of videos. We expect the contribution focusing on the innovative few-shot techniques, including methodological and algorithmic methods to solve the theoretical and practical multimedia problems. We also encourage the contributions on various deployable few-shot applications.

Topics of interest include but are not limited to:

Important dates

Manuscript submission deadline: November 15, 2021

Decision notification: December 30, 2021

Author revisions due (if applicable): January 30, 2022

Final decision notification: February 28, 2022

Submission Guidelines

Papers submitted to this special issue must be original and must not be under consideration for publication in any other journal or conference. The manuscripts will be peer-reviewed strictly following the reviewing procedures. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. The papers must be written in English and must not exceed 30 pages (single column, double space, 12 pt font, including figures, tables, and references). Authors should prepare their manuscript according to the journal's Submission Guidelines at https://www.springer.com/journal/530 

Other Special Issues on this journal

Publisher
Journal Details
Closing date
G2R Score
Few-shot Learning for Intelligent Multimedia Systems

Few-shot Learning for Intelligent Multimedia Systems

Multimedia Systems
Closing date: 15-11-2021 G2R Score: 1.81
Few-shot Learning for Intelligent Multimedia Systems

Few-shot Learning for Intelligent Multimedia Systems

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Closing date: 15-11-2021 G2R Score: 1.81
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Closing date: 30-10-2021 G2R Score: 1.81

Closed Special Issues

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Closing date
G2R Score
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Closing date: 15-09-2021 G2R Score: 1.81
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Closing date: 15-09-2021 G2R Score: 1.81
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Deep Learning for Multimedia Healthcare

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Closing date: 15-12-2020 G2R Score: 1.81
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Multimedia Systems
Closing date: 15-10-2020 G2R Score: 1.81
Deep learning methods for cyber bullying detection in multi-modal data

Deep learning methods for cyber bullying detection in multi-modal data

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Closing date: 30-07-2020 G2R Score: 1.81
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Closing date: 15-07-2020 G2R Score: 1.81
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Low complexity methods for multimedia security

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Closing date: 15-06-2020 G2R Score: 1.81